Hands-on Exercise 3

3  Programming Interactive Data Visualisation with R

3.1 Learning Outcome

In this hands-on exercise, you will learn how to create interactive data visualisation by using functions provided by ggiraph and plotlyr packages.

3.2 Getting Started

First, write a code chunk to check, install and launch the following R packages:

• ggiraph for making ‘ggplot’ graphics interactive.

• plotly, R library for plotting interactive statistical graphs.

• DT provides an R interface to the JavaScript library DataTables that create interactive table on html page.

• tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.

• patchwork for combining multiple ggplot2 graphs into one figure.

The code chunk below will be used to accomplish the task.

pacman::p_load(ggiraph, plotly, 
               patchwork, DT, tidyverse)  

3.3 Importing Data

In this section, Exam_data.csv provided will be used. Using read_csv() of readr package, import Exam_data.csv into R.

The code chunk below read_csv() of readr package is used to import Exam_data.csv data file into R and save it as an tibble data frame called exam_data.

exam_data <- read_csv("data/Exam_data.csv")

3.4 Interactive Data Visualisation - ggiraph methods

ggiraph is an htmlwidget and a ggplot2 extension. It allows ggplot graphics to be interactive.

Interactive is made with ggplot geometries that can understand three arguments:

• Tooltip: a column of data-sets that contain tooltips to be displayed when the mouse is over elements.

• Onclick: a column of data-sets that contain a JavaScript function to be executed when elements are clicked.

• Data_id: a column of data-sets that contain an id to be associated with elements.

If it used within a shiny application, elements associated with an id (data_id) can be selected and manipulated on client and server sides. Refer to this article for more detail explanation.

3.4.1 Tooltip effect with tooltip aesthetic

Below shows a typical code chunk to plot an interactive statistical graph by using ggiraph package. Notice that the code chunk consists of two parts. First, an ggplot object will be created. Next, girafe() of ggiraph will be used to create an interactive svg object.

The plot

The code

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = ID),
    stackgroups = TRUE, 
    binwidth = 1, 
    method = "histodot") +
  scale_y_continuous(NULL, 
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

3.5.1 Displaying multiple information on tooltip

The content of the tooltip can be customised by including a list object as shown in the code chunk below.

exam_data$tooltip <- c(paste0(     
  "Name = ", exam_data$ID,         
  "\n Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = exam_data$tooltip), 
    stackgroups = TRUE,
    binwidth = 1,
    method = "histodot") +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)

The first three lines of codes in the code chunk create a new field called tooltip. At the same time, it populates text in ID and CLASS fields into the newly created field. Next, this newly created field is used as tooltip field as shown in the code of line 7.

3.6 Interactivity

By hovering the mouse pointer on an data point of interest, the student’s ID and Class will be displayed.

3.6.1 Customising Tooltip style

Code chunk below uses opts_tooltip() of ggiraph to customize tooltip rendering by add css declarations.

tooltip_css <- "background-color:white; 
font-weight:bold; color:black;"

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = ID),                   
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(    #<<
    opts_tooltip(    #<<
      css = tooltip_css)) #<<
)                                        

Notice that the background colour of the tooltip is white and the font colour is black and bold.

• Refer to Customizing girafe objects to learn more about how to customise ggiraph objects.

3.6.2 Displaying statistics on tooltip

Code chunk below shows an advanced way to customise tooltip. In this example, a function is used to compute 90% confident interval of the mean. The derived statistics are then displayed in the tooltip.

tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, size = 0.2
  )

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

3.6.3 Hover effect with data_id aesthetic

Code chunk below shows the second interactive feature of ggiraph, namely data_id.

exam_data$tooltip <- c(paste0(     
  "Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(data_id = CLASS, tooltip = exam_data$tooltip),             
    stackgroups = TRUE,               
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618                      
)       

Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.

Note that the default value of the hover css is hover_css = “fill:orange;”.

3.6.4 Styling hover effect

In the code chunk below, css codes are used to change the highlighting effect.

exam_data$tooltip <- c(paste0(     
  "Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = CLASS, tooltip = exam_data$tooltip),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.

Note: Different from previous example, in this example the ccs customisation request are encoded directly.

3.6.5 Combining tooltip and hover effect

There are time that we want to combine tooltip and hover effect on the interactive statistical graph as shown in the code chunk below. This is another way of doing it as compared to the code chunk above.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = CLASS, 
        data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over. At the same time, the tooltip will show the CLASS.

3.6.6 Click effect with onclick

onclick argument of ggiraph provides hotlink interactivity on the web.

The code chunk below shown an example of onclick.

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)                                        

Interactivity: Web document link with a data object will be displayed on the web browser upon mouse click.

Warning

Note that click actions must be a string column in the dataset containing valid javascript instructions.

3.6.7 Coordinated Multiple Views with ggiraph

Coordinated multiple views methods has been implemented in the data visualisation below.

Notice that when a data point of one of the dotplot is selected, the corresponding data point ID on the second data visualisation will be highlighted too.

In order to build a coordinated multiple views as shown in the example above, the following programming strategy will be used:

1. Appropriate interactive functions of ggiraph will be used to create the multiple views.

2. patchwork function of patchwork package will be used inside girafe function to create the interactive coordinated multiple views.

The data_id aesthetic is critical to link observations between plots and the tooltip aesthetic is optional but nice to have when mouse over a point.

The plot

The code

p1 <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = ID, tooltip = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +  
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

p2 <- ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_dotplot_interactive(              
    aes(data_id = ID, tooltip = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + 
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

girafe(code = print(p1 + p2), 
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
         )
       ) 

Alternative chart where the scale is manually adjusted for better visibility.

The plot

The code

# Stack manually for Maths
exam_data_math <- exam_data %>%
  group_by(MATHS) %>%
  mutate(stack_y = row_number())

# Stack manually for English
exam_data_eng <- exam_data %>%
  group_by(ENGLISH) %>%
  mutate(stack_y = row_number())

# Plot Maths
p1 <- ggplot(exam_data_math, aes(x = MATHS, y = stack_y)) +
  geom_point_interactive(aes(data_id = ID, tooltip = ID), size = 1.5) +
  scale_y_continuous(NULL, breaks = NULL) +
  coord_cartesian(xlim = c(0, 100)) +
  theme_minimal()

# Plot English
p2 <- ggplot(exam_data_eng, aes(x = ENGLISH, y = stack_y)) +
  geom_point_interactive(aes(data_id = ID, tooltip = ID), size = 1.5) +
  scale_y_continuous(NULL, breaks = NULL) +
  coord_cartesian(xlim = c(0, 100)) +
  theme_minimal()

# Combine interactive
girafe(
  code = print(p1 + p2),
  width_svg = 6,
  height_svg = 3,
  options = list(
    opts_hover(css = "fill: #202020;"),
    opts_hover_inv(css = "opacity:0.2;")
  )
)

3.7 Interactive Data Visualisation - plotly methods!

Plotly’s R graphing library create interactive web graphics from ggplot2 graphs and/or a custom interface to the (MIT-licensed) JavaScript library plotly.js inspired by the grammar of graphics. Different from other plotly platform, plot.R is free and open source.

There are two ways to create interactive graph by using plotly, they are:

• by using plot_ly(), and

• by using ggplotly()

3.7.1 Creating an interactive scatter plot: plot_ly() method

The tabset below shows an example a basic interactive plot created by using plot_ly().

The plot

The code

plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

3.7.2 Working with visual variable: plot_ly() method

In the code chunk below, color argument is mapped to a qualitative visual variable (i.e. RACE).

The plot

The code

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE)

3.7.3 Creating an interactive scatter plot: ggplotly() method

The code chunk below plots an interactive scatter plot by using ggplotly().

The plot

The code

p <- ggplot(data=exam_data, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
ggplotly(p)

3.7.4 Coordinated Multiple Views with plotly

The creation of a coordinated linked plot by using plotly involves three steps:

• highlight_key() of plotly package is used as shared data.

• two scatterplots will be created by using ggplot2 functions.

• lastly, subplot() of plotly package is used to place them next to each other side-by-side.

The plot

Click on a data point of one of the scatterplot and see how the corresponding point on the other scatterplot is selected.

The code

d <- highlight_key(exam_data)
p1 <- ggplot(data=d, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

p2 <- ggplot(data=d, 
            aes(x = MATHS,
                y = SCIENCE)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
subplot(ggplotly(p1),
        ggplotly(p2))

However, the chart did not include the title and axis. The chart is adjusted as follows below:

The plot

Click on a data point of one of the scatterplot and see how the corresponding point on the other scatterplot is selected.

The code

d <- highlight_key(exam_data)

p1 <- ggplot(data = d, 
             aes(x = MATHS, y = ENGLISH)) +
  geom_point(size = 1) +
  labs(
    x = "Maths Score",
    y = "English Score"
  ) +
  coord_cartesian(xlim = c(0, 100), ylim = c(0, 100)) +
  theme_minimal()+
  theme(
    axis.title = element_text(size = 10))

p2 <- ggplot(data = d, 
             aes(x = MATHS, y = SCIENCE)) +
  geom_point(size = 1) +
  labs(
    x = "Maths Score",
    y = "Science Score"
  ) +
  coord_cartesian(xlim = c(0, 100), ylim = c(0, 100)) +
  theme_minimal()+
  theme(
    axis.title = element_text(size = 10))

# Combine plots
subplot(ggplotly(p1), ggplotly(p2), titleX = TRUE, titleY = TRUE,
  margin = 0.07 ) %>%
  layout(
    annotations = list(
      list(
        text = "Maths vs English Scores",   # Title for 1st plot
        x = 0.225, y = 1.05, showarrow = FALSE,
        xref = "paper", yref = "paper",
        font = list(size = 14), xanchor = "center"
      ),
      list(
        text = "Maths vs Science Scores",   # Title for 2nd plot
        x = 0.775, y = 1.05, showarrow = FALSE,
        xref = "paper", yref = "paper",
        font = list(size = 14), xanchor = "center"
      )
    )
  )

Thing to learn from the code chunk:

• highlight_key() simply creates an object of class crosstalk::SharedData.

• Visit this link to learn more about crosstalk,

3.8 Interactive Data Visualisation - crosstalk methods!

Crosstalk is an add-on to the htmlwidgets package. It extends htmlwidgets with a set of classes, functions, and conventions for implementing cross-widget interactions (currently, linked brushing and filtering).

3.8.1 Interactive Data Table: DT package

• A wrapper of the JavaScript Library DataTables

• Data objects in R can be rendered as HTML tables using the JavaScript library ‘DataTables’ (typically via R Markdown or Shiny).

DT::datatable(exam_data, class= "compact")

3.8.2 Linked brushing: crosstalk method

The plot

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

The code

Code chunk below is used to implement the coordinated brushing shown in the plot.

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

Things to learn from the code chunk:

highlight() is a function of plotly package. It sets a variety of options for brushing (i.e., highlighting) multiple plots. These options are primarily designed for linking multiple plotly graphs, and may not behave as expected when linking plotly to another htmlwidget package via crosstalk. In some cases, other htmlwidgets will respect these options, such as persistent selection in leaflet.

bscols() is a helper function of crosstalk package. It makes it easy to put HTML elements side by side. It can be called directly from the console but is especially designed to work in an R Markdown document. Warning: This will bring in all of Bootstrap!.

3.9 Reference

3.9.1 ggiraph

This link provides online version of the reference guide and several useful articles. Use this link to download the pdf version of the reference guide.

• How to Plot With Ggiraph

• Interactive map of France with ggiraph

• Custom interactive sunbursts with ggplot in R

• This link provides code example on how ggiraph is used to interactive graphs for Swiss Olympians - the solo specialists.

3.9.2 plotly for R

• Getting Started with Plotly in R

• A collection of plotly R graphs are available via this link.

• Carson Sievert (2020) Interactive web-based data visualization with R, plotly, and shiny, Chapman and Hall/CRC is the best resource to learn plotly for R. The online version is available via this link

• Plotly R Figure Reference provides a comprehensive discussion of each visual representations.

• Plotly R Library Fundamentals is a good place to learn the fundamental features of Plotly’s R API.

• Getting Started

• Visit this link for a very interesting implementation of gganimate by your senior.

• Building an animation step-by-step with gganimate.

• Creating a composite gif with multiple gganimate panels

3.10 Additional Plots

These are the additional plots which are beyond the scope of the hand-on exercises for my learning purposes.

3.10.1 Additional Plots Interactive map of Singapore using ggiraph and plotlyr

The Singapore’s map shapefile is downloaded from URA’s Master Plan 2014 Planning Area Boundary.

The libraries and codes used for data pre-processing and visualisation are shown below:

library(sf)
library(ggplot2)
library(ggiraph)
library(dplyr)

sg_map <- st_read("data/Map/MP14_PLNG_AREA_WEB_PL.shp")

Reading layer `MP14_PLNG_AREA_WEB_PL' from data source 
  `C:\Users\Belinda\OneDrive - Singapore Management University\Desktop\Visual Analytics\belindalim\ISSS608-VAA\Hands-on_Ex\Hands-on_Ex03\data\Map\MP14_PLNG_AREA_WEB_PL.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 55 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

sg_map <- sg_map %>%
  mutate(tooltip = paste0("Planning Area: ", PLN_AREA_N))

gg <- ggplot() +
  geom_sf_interactive(
    data = sg_map,
    aes(geometry = geometry, tooltip = tooltip, data_id = PLN_AREA_N),
    fill = "lightblue", color = "white"
  ) +
  theme_minimal()

girafe(ggobj = gg, width_svg = 6, height_svg = 3)

3.10.2 Interactive map of Singapore based on percentage of Population aged 65 and above by Planning Areas (PA)

First, the percentage of Population aged 65 and above by PA is computed and joined to the map.

The codes used for data pre-processing are shown below:

pop_data <- read_csv("data/Demographic_data.csv", show_col_types = FALSE)

pop_summary <- pop_data %>%
  mutate(
    Age_DR = if_else(Age == "90_and_Over", "90", as.character(Age)),
    Age_DR = as.numeric(Age_DR),
    Pop_DR = as.numeric(Pop)
  ) %>%
  group_by(PA) %>%
  summarise(
    Pop65 = sum(Pop_DR[Age_DR >= 65], na.rm = TRUE),
    TotalPop = sum(Pop_DR, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  mutate(PctOver65 = round(Pop65 / TotalPop * 100, 1))

sg_map <- sg_map %>%
  mutate(PA = toupper(trimws(PLN_AREA_N)))

pop_summary <- pop_summary %>%
  mutate(PA = toupper(trimws(PA)))

sg_map_joined <- sg_map %>%
  left_join(pop_summary, by = "PA")

The choropleth map with percentage of elderly population aged 65 and above is created using the codes shown below:

p <- ggplot(sg_map_joined) +
  geom_sf(aes(
    fill = PctOver65,
    text = paste0("Planning Area: ", PA, "\nAge 65+ Population: ", PctOver65, "%")
  ), color = "white", linewidth = 0.3) +
  scale_fill_gradient(
    low = "#ffe0b2",   
    high = "#b30000",  
    na.value = "grey90") +
  labs(
    title = "% of Population Aged 65+ by PA",
    fill = "% Aged 65+"
  ) +
  theme_void() + theme(panel.border = element_blank()) 

ggplotly(p, tooltip = "text")